This project is a community effort, and everyone is welcome to contribute.

The project is hosted on


A large part of our guidelines come directly from scikit-learn. For more detail, refer to the documentation there.

Submitting a bug report

In case you experience issues using this package, do not hesitate to submit a ticket to the Bug Tracker. You are also welcome to post feature requests or links to pull requests.

Retrieving the latest code

We use Git for version control and GitHub for hosting our main repository.

You can check out the latest sources with the command:

git clone git://

Contributing code


To avoid duplicating work, it is highly advised that you contact the developers on GitHub before starting work on a non-trivial feature.

Opening a pull request with a [WIP] prefix usually serves to inform the other developers that there is work happening, and any discussion can happen there. For example, a pull request with the title [WIP]World’s best classifier would let us know you are implementing the world’s best classifier.

See the section below for more details on opening pull requests and contributing to the project.

How to contribute

The preferred way to contribute to sklearn-theano is to fork the main repository on GitHub, then submit a “pull request” (PR):

  1. Create an account on GitHub if you do not already have one.

  2. Fork the project repository: click on the ‘Fork’ button near the top of the page. This creates a copy of the code under your account on the GitHub server.

  3. Clone this copy to your local disk:

    $ git clone
  4. Create a branch to hold your changes:

    $ git checkout -b my-feature

    and start making changes. Never work in the master branch!

  5. Work on this copy, on your computer, using Git to do the version control. When you’re done editing, do:

    $ git add modified_files
    $ git commit

    to record your changes in Git, then push them to GitHub with:

    $ git push -u origin my-feature

Finally, go to the web page of the your fork of the scikit-learn repo, and click ‘Pull request’ to send your changes to the maintainers for review. request. This will send an email to the committers, but might also send an email to the mailing list in order to get more visibility.


In the above setup, your origin remote repository points to YourLogin/sklearn-theano.git. If you wish to fetch/merge from the main repository instead of your forked one, you will need to add another remote to use instead of origin. If we choose the name upstream for it, the command will be:

$ git remote add upstream

(If any of the above seems like magic to you, then look up the Git documentation on the web.)

It is recommended to check that your contribution complies with the following rules before submitting a pull request:

  • Follow the coding-guidelines (see below).

  • All public methods should have informative docstrings with sample usage presented as doctests when appropriate.

  • All other tests pass when everything is rebuilt from scratch. On Unix-like systems, check with (from the toplevel source folder):

    $ make
  • When adding additional functionality, provide at least one example script in the examples/ folder. Have a look at other examples for reference. Examples should demonstrate why the new functionality is useful in practice and, if possible, compare it to other methods available in sklearn-theano.

  • At least one paragraph of narrative documentation with links to references in the literature (with PDF links when possible) and the example.

You can also check for common programming errors with the following tools:

  • Code with a good unittest coverage (at least 90%, better 100%), check with:

    $ pip install nose coverage
    $ nosetests --with-coverage path/to/tests_for_package

    see also testing_coverage

  • No pyflakes warnings, check with:

    $ pip install pyflakes
    $ pyflakes path/to/
  • No PEP8 warnings, check with:

    $ pip install pep8
    $ pep8 path/to/
  • AutoPEP8 can help you fix some of the easy redundant errors:

    $ pip install autopep8
    $ autopep8 path/to/

Bonus points for contributions that include a performance analysis with a benchmark script and profiling output (please report on the mailing list or on the GitHub wiki).


The current state of the sklearn-theano code base is not compliant with all of those guidelines, but we expect that enforcing those constraints on all contributions will get the overall code base quality in the right direction.


For two very well documented and more detailed guides on development workflow, please pay a visit to the Scipy Development Workflow - and the Astropy Workflow for Developers sections.


We are glad to accept any sort of documentation: function docstrings, reStructuredText documents (like this one), tutorials, etc. reStructuredText documents live in the source code repository under the doc/ directory.

You can edit the documentation using any text editor, and then generate the HTML output by typing make html from the doc/ directory. Alternatively, make html-noplot can be used to quickly generate the documentation without the example gallery. The resulting HTML files will be placed in _build/html/ and are viewable in a web browser. See the README file in the doc/ directory for more information.

For building the documentation, you will need sphinx, matplotlib and pillow.

When you are writing documentation, it is important to keep a good compromise between mathematical and algorithmic details, and give intuition to the reader on what the algorithm does.

Any math and equations, followed by references, can be added to further the documentation. Not starting the documentation with the maths makes it more friendly towards users that are just interested in what the feature will do, as opposed to how it works “under the hood”.


Sphinx version

While we do our best to have the documentation build under as many version of Sphinx as possible, the different versions tend to behave slightly differently. To get the best results, you should use version 1.0.

Issue Tracker Tags

All issues and pull requests on the Github issue tracker should have (at least) one of the following tags:

Bug / Crash:Something is happening that clearly shouldn’t happen. Wrong results as well as unexpected errors from estimators go here.
Cleanup / Enhancement:
 Improving performance, usability, consistency.
Documentation:Missing, incorrect or sub-standard documentations and examples.
New Feature:Feature requests and pull requests implementing a new feature.

There are two other tags to help new contributors:

Easy:This issue can be tackled by anyone, no experience needed. Ask for help if the formulation is unclear.
Moderate:Might need some knowledge of machine learning or the package, but is still approachable for someone new to the project.

Other ways to contribute

Code is not the only way to contribute to sklearn-theano. For instance, documentation is also a very important part of the project and often doesn’t get as much attention as it deserves. If you find a typo in the documentation, or have made improvements, do not hesitate to send an email to the mailing list or submit a GitHub pull request. Full documentation can be found under the doc/ directory.

It also helps us if you spread the word: reference the project from your blog and articles, link to it from your website, or simply say “I use it”:

Coding guidelines

The following are some guidelines on how new code should be written. Of course, there are special cases and there will be exceptions to these rules. However, following these rules when submitting new code makes the review easier so new code can be integrated in less time.

Uniformly formatted code makes it easier to share code ownership. The scikit-learn project tries to closely follow the official Python guidelines detailed in PEP8 that detail how code should be formatted and indented. Please read it and follow it.

In addition, we add the following guidelines:

  • Use underscores to separate words in non class names: n_samples rather than nsamples.
  • Avoid multiple statements on one line. Prefer a line return after a control flow statement (if/for).
  • Use relative imports for references inside scikit-learn.
  • Unit tests are an exception to the previous rule; they should use absolute imports, exactly as client code would. A corollary is that, if exports a class or function that is implemented in, the test should import it from
  • Please don’t use ``import *`` in any case. It is considered harmful by the official Python recommendations. It makes the code harder to read as the origin of symbols is no longer explicitly referenced, but most important, it prevents using a static analysis tool like pyflakes to automatically find bugs in sklearn-theano.
  • Use the numpy docstring standard in all your docstrings.

A good example of code that we like can be found here.

Additional Help

See the scikit-learn developer documentation for more details. In general, we try to follow the scikit-learn guidelines as closely as possible.

Working notes

For unresolved issues, TODOs, and remarks on ongoing work, developers are advised to maintain notes on the GitHub wiki.